Software frameworks for neural networks play a key role in the development and application of deep learning methods. In this paper, we introduce the Chainer framework, which intends to provide a flexible, intuitive, and high performance means of implementing the full range of deep learning models needed by researchers and practitioners. Chainer provides acceleration using Graphics Processing Units with a familiar NumPy-like API through CuPy, supports general and dynamic models in Python through Define-by-Run, and also provides add-on packages for state-of-the-art computer vision models as well as distributed training.
@article{arxiv.1908.00213,
title = {Chainer: A Deep Learning Framework for Accelerating the Research Cycle},
author = {Seiya Tokui and Ryosuke Okuta and Takuya Akiba and Yusuke Niitani and Toru Ogawa and Shunta Saito and Shuji Suzuki and Kota Uenishi and Brian Vogel and Hiroyuki Yamazaki Vincent},
journal= {arXiv preprint arXiv:1908.00213},
year = {2019}
}